Basics of Graphics

Today we will…

  • Accessing Practice Activities
  • Review
    • Tidy Data
    • Load External Data
    • Graphics (and ggplot2)
  • PA 3: Using Data Visualization to Find the Penguins
    • Peer Programming

Grammar of Graphics

Grammar of Graphics

The Grammar of Graphics (GoG) is a principled way of specifying exactly how to create a particular graph from a given data set. It helps us to systematically design new graphs.


Think of a graph or a data visualization as a mapping…

…FROM variables in the data set (or statistics computed from the data)…

…TO visual attributes (or “aesthetics”) of marks (or “geometric elements”) on the page/screen.

Why GoG?

  • It’s more flexible than a “chart zoo” of named graphs.
  • The software understands the structure of your graph.
  • It easily automates graphing of data subsets.

Components of GoG

  • data: dataframe containing variables
  • aes : aesthetic mappings (position, color, symbol, …)
  • geom : geometric element (point, line, bar, box, …)
  • stat : statistical variable transformation (identity, count, linear model, quantile, …)
  • scale : scale transformation (log scale, color mapping, axes tick breaks, …)
  • coord : Cartesian, polar, map projection, …
  • facet : divide into subplots using a categorical variable

Using ggplot2

How to Build a Graphic

Complete this template to build a basic graphic:

ggplot(
  data = <DATA>, 
  mapping = aes(<MAPPINGS>)
  ) +
  <GEOM FUNCTION>() + 
  any other arguments...

Notice, every + adds another layer to our graphic.

Also notice that I’m using named arguments to make my code easier to read.

This begins a plot that you can add layers to:

ggplot(data = mpg)

An image of a blank gray square representing a blank plotting canvas.

ggplot(data = mpg, 
       mapping = aes(x = class, y = hwy)
       )

An image of a gray square with white gridlines representing a plotting canvas where the variables have been assigned to the x and y aesthetics. On the x-axis there is a variable named 'class' with seven different values mapped to a particular white gridline---2seater, compact, midsize, minivan, pickup, subcompact, suv. On the y-axis there is a variable named 'hwy' with three printed values mapped to different white gridlines---20, 30, and 40. The spaces between these gridlines represent values between the printed values (e.g., 25).

ggplot(data = mpg, 
       mapping = aes(x = class, 
                     y = hwy)
       ) +
  geom_jitter()

A visualization of the different hwy milage for different types of cars. On the x-axis there is a variable named 'class' with seven different values mapped to a particular white gridline---2seater, compact, midsize, minivan, pickup, subcompact, suv. On the y-axis there is a variable named 'hwy' with three printed values mapped to different white gridlines---20, 30, and 40. The spaces between these gridlines represent values between the printed values (e.g., 25). The visualization has a variety of points (black dots), where each point represents a particular vehicle. Each point is horizontally aligned with the type of vehicle and vertically aligned with the highway mileage for the vehicle. Overall, it appears that pickups and suvs have the lowest highway mileage, with minivans having slightly better milage. compact, midsize, and subcompact vehicles have fairly similar milage, with cars ranging from 25 to 40 miles per gallon.

ggplot(data = mpg, 
       mapping = aes(x = class, 
                     y = hwy)
       ) +
  geom_jitter() +
  geom_boxplot()

The same visualization is presented, except there are not boxplots superimposed on top of the points (dots) for each vehicle type. The boxplots display the median (center line), quantiles (edges), and min / max (whiskers) of each vehicle's mileage.

Aesthetics

We map variables (columns) from the data to aesthetics on the graphic using the aes() function.

What aesthetics can we set (see ggplot2 cheat sheet for more)?

  • x, y
  • color, fill
  • linetype
  • size
  • shape

Aesthetics

We map variables (columns) from the data to aesthetics on the graphic using the aes() function.

What aesthetics can we set (see ggplot2 cheat sheet for more)?

  • x, y
  • color, fill
  • linetype
  • size
  • shape

Special Properties of Aesthetics

ggplot(data = housingsub, 
       mapping = aes(
         x = date, 
         y = median)
       ) +
  geom_point()
ggplot(data = housingsub) +
  geom_point(mapping = aes(x = date,
                           y = median)
             )

Special Properties of Aesthetics

ggplot(data = housingsub) +
  geom_point(mapping = aes(x = date, 
                           y = median,
                           color = city)
             )
ggplot(data = housingsub) +
  geom_point(mapping = aes(x = date, 
                           y = median), 
             color = "blue")

Geometric Objects

We use a geom_XXX() function to represent data points.

one variable

  • geom_density()
  • geom_dotplot()
  • geom_histogram()
  • geom_boxplot()

two variable

  • geom_point()
  • geom_line()
  • geom_density_2d()

three variable

  • geom_contour()
  • geom_raster()


This is not an exhaustive list – see ggplot2 cheat sheet.

Creating a Graphic

To create a specific type of graphic, we will combine aesthetics and geometric objects.

Faceting

  • facet_wrap(~ b): facets by one variable
    • nrow controls the number of rows the facets are output into
    • ncol controls the number of columns the facets are output into
  • facet_grid(a ~ b): facet by two variables
    • variable a will be assigned to the rows
    • variable b will be assigned to the columns into both rows and columns

You can set scales to let axis limits vary across facets:

facet_grid(y ~ x, scales = ______)

  • "free" – both x- and y-axis limits adjust to individual facets
  • "free_x" – only x-axis limits adjust
  • "free_y" – only y-axis limits adjust

Statistical Transformation: stat

A stat transforms an existing variable into a new variable to plot.

  • identity leaves the data as is.
  • count counts the number of observations.
  • summary allows you to specify a desired transformation function.

Sometimes these statistical transformations happen under the hood when we use a specific geom_XXX().

Statistical Transformation: stat

ggplot(data = mpg,
       mapping = aes(x = class)
       ) +
  geom_bar()

ggplot(data = mpg,
       mapping = aes(x = class)
       ) +
  stat_count(geom = "bar")

ggplot(data = mpg,
       mapping = aes(x = class,
                     y = hwy)
       ) +
  stat_summary(geom = "bar",
               fun = "mean") +
  scale_y_continuous(limits = c(0, 45))

ggplot(data = mpg,
       mapping = aes(x = class,
                     y = hwy)
       ) +
  stat_summary(geom = "bar",
               fun = "max") +
  scale_y_continuous(limits = c(0, 45))

Position Adjustements

Position adjustments determine how to arrange geom’s that would otherwise occupy the same space.

  • position = "dodge": Arrange elements side by side.
  • position = "fill": Stack elements on top of one another + normalize height.
  • position = "stack": Stack elements on top of one another.
  • position = "jitter": Add random noise to x & y position of each element to avoid overplotting (see geom_jitter()).

Position Adjustements

ggplot(data = mpg, 
       mapping = aes(fl, fill = drv)
       ) + 
  geom_bar(position = "_____")

Plot Customizations

Code 
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, color = cyl)
       ) + 
  geom_jitter() + 
  labs(x = "Engine Displacement (liters)", 
       y = " ", 
       color = "Cylinders",
       title = "Cars with More Cylinders Have Larger Engine Displacement\n and Lower Fuel Efficiency")

Code 
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, color = cyl)
       ) + 
  geom_jitter() + 
  labs(x = "Engine Displacement (liters)", 
       y = " ", 
       color = "Cylinders",
       title = "Cars with More Cylinders Have Larger Engine Displacement\n and Lower Fuel Efficiency") +
  theme_bw() +
  theme(legend.position = "bottom")

Code 
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, color = cyl)
       ) + 
  geom_jitter() + 
  labs(x = "Engine Displacement (liters)", 
       y = " ", 
       color = "Cylinders",
       title = "Cars with More Cylinders Have Larger Engine Displacement\n and Lower Fuel Efficiency") +
  scale_y_continuous(limits = c(0, 50),
                     breaks = seq(from = 0, to = 50, by = 5)
                     )

Code 
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, color = cyl)
       ) + 
  geom_jitter() + 
  labs(x = "Engine Displacement (liters)", 
       y = " ", 
       color = "Cylinders",
       title = "Cars with More Cylinders Have Larger Engine Displacement\n and Lower Fuel Efficiency") +
  scale_color_gradient(low = "white", high = "green4")

Nicely Formatted Code

ggplot Code

It is good practice to put each geom and aes on a new line.

  • This makes code easier to read!
  • Generally: no line of code should be over 80 characters long.
  • Pro Tip: The styler package can do this for you!
ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = class)) + geom_point() + theme_bw() + labs(x = "City (mpg)", y = "Highway (mpg)")
ggplot(data = mpg, 
       mapping = aes(x = cty, y = hwy, color = class)) + 
  geom_point() + 
  theme_bw() + 
  labs(x = "City (mpg)", y = "Highway (mpg)")
ggplot(data = mpg, 
       mapping = aes(
         x = cty, 
         y = hwy, 
         color = class
         )
       ) + 
  geom_point() + 
  theme_bw() + 
  labs(x = "City (mpg)", 
       y = "Highway (mpg)")

Let’s Make Some Visualizations!

PA 3: Using Data Visualization to Find the Penguins

Artwork by Allison Horst

Using Data Visualization to Find the Penguins

This puzzle activity will require knowledge of:

  • installing and loading packages in R
  • formatting code chunks in Quarto
  • interpreting the context of a dataset
  • data types / variable types
  • different types of visualizations
  • what visualization(s) go with different data types
  • how to make visualizations with ggplot2
  • choosing between different aesthetic options

None of us have all these abilities. Each of us has some of these abilities.

Pair Programming Expectations

During your collaboration, you and your partner will alternate between two roles:

Developer

  • Reads prompt and ensures Coder understands what is being asked.
  • Types the code specified by the Coder into the Quarto document.
  • Runs the code provided by the Coder.
  • Works with Coder to debug the code.
  • Evaluates the output.
  • Works with Coder to write code comments.

Coder

  • Reads out instructions or prompts
  • Directs the Developer what to type.
  • Talks with Developer about their ideas.
  • Manages resources (e.g., cheatsheets, textbook).
  • Works with Developer to debug the code.
  • Works with Developer to write code comments.

Group Norms

  1. Think and work together. Do not divide the work.
  2. You are smarter together.
  3. Be open minded.
  4. No cross-talk with other groups.
  5. Communicate with each other!

ggplot2 Resources

Every group should have a ggplot2 cheatsheet!

On the Front

  • Column 1: the “template” for making a ggplot
  • Column 3: creating plots for two continuous variables
  • Column 4: creating plots for one discrete or one continuous variable

On the Back

  • Column 4: adding facets and labels to your plot
  • Column 3: adding themes to your plot (if you have extra time)

A picture of the ggplot2 cheatsheet, which contains helpful information on assembling a variety of visualizations all using the ggplot2 package.

Opening the Practice Activity in Posit Cloud

The partner whose family name starts first alphabetically starts as the Developer!

Remember:

  • The Developer is responsible for either setting up a shared project on the Server or shared repository on GitHub.

Submission

  • When you have completed the visualization tasks, you will work as a group to answer the five questions posed at the end of the document.

  • Each person will input the answers to these questions in the PA3 Canvas quiz and submit either a link or zipped version of the project.

  • For those recording, you can press the X (top right corner) and wait until the video finishes uploading.

  • Once finished, you can start work on Lab 3!

Wrap-up

What skills did you need?